Meeting Banner
Abstract #4777

Probabilistic Optimization of Cartesian k-Space Undersampling Patterns for Learning-Based  Reconstruction

Valery Vishnevskiy1, Jonas Walheim1, and Sebastian Kozerke1

1Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

Learning-based methods offer improved reconstruction accuracy for compressed Sensing MRI. However, most modern methods assume the sampling trajectory to be predefined. In order to further increase reconstruction quality, we present a method for adaptive design of Cartesian undersampling masks. The proposed method delivers sampling trajectories that allow to improve reconstruction accuracy by 26% and 6% compared to the random and state-of-the-art interleaved variable density patterns, respectively.

This abstract and the presentation materials are available to members only; a login is required.

Join Here